<i>In silico</i> approaches and tools for the prediction of drug metabolism and fate: A review

Kazmi, Sayada Reemsha,Jun, Ren,Yu, Myeong-Sang,Jung, Chanjin,Na, Dokyun Elsevier 2019 Computers in biology and medicine Vol.106 No.-

<P><B>Abstract</B></P> <P>The fate of administered drugs is largely influenced by their metabolism. For example, endogenous enzyme–catalyzed conversion of drugs may result in therapeutic inactivation or activation or may transform the drugs into toxic chemical compounds. This highlights the importance of drug metabolism in drug discovery and development, and accounts for the wide variety of experimental technologies that provide insights into the fate of drugs. In view of the high cost of traditional drug development, a number of computational approaches have been developed for predicting the metabolic fate of drug candidates, allowing for screening of large numbers of chemical compounds and then identifying a small number of promising candidates. In this review, we introduce <I>in silico</I> approaches and tools that have been developed to predict drug metabolism and fate, and assess their potential to facilitate the virtual discovery of promising drug candidates. We also provide a brief description of various recent models for predicting different aspects of enzyme-drug reactions and provide a list of recent <I>in silico</I> tools used for drug metabolism prediction.</P> <P><B>Highlights</B></P> <P> <UL> <LI> <I>In silico</I> approaches and tools for predicting drug metabolism and fate are reviewed. </LI> <LI> QSAR, machine learning, and computational docking/molecular dynamics are described. </LI> <LI> Computational models for predicting metabolic enzymatic reactions are summarized. </LI> <LI> A list of tools for <I>in silico</I> prediction is provided. </LI> <LI> Concerns and limitations of predictive model construction are outlined. </LI> </UL> </P>

초기 신약개발 단계에서의 신약후보물질 최적화를 위한 물성 스크리닝 방법 검증 및 프로파일링

이진석(Jinseok Lee),안성훈(Sung-Hoon Ahn) 대한약학회 2018 약학회지 Vol.62 No.4

Drug-like properties based on physicochemical properties are important to confer good ADME/PK char-acteristics for sufficiently effective efficacy and safety on preclinical and clinical trials. Therefore, accurate estimation and optimization of physicochemical properties such as ionization constant, lipophilicity, permeability, and solubility are import-ant factors for pharmacokinetic properties including clearance, half-life, bioavailability, drug-drug interactions. This study was performed to validate screening method of physicochemical properties. The commercially available drugs were used to validate analytical method system of physicochemical properties and experimental values were compared with literature values and in silico predictions. The experimental pKa values were in very good accordance with literature values in both case of pKa PRO (r² = 0.82) and GLpKa (r² = 0.87). Experimental values and in silico predictions of lipophilicity were also in very good accordance with literature values (r² > 0.82). Experimental physicochemical values of KR-62980 as a new drug candidate showed similar values to in silico predictions. Finally, screenings of physicochemical properties can be applied well and this result may cause lowering cost and accelerating screening speed for the integration of ADME/PK screening data in early stage of new drug discovery.

Semi-quantitative models for identifying potent and selective transthyretin amyloidogenesis inhibitors

Connelly, Stephen,Mortenson, David E.,Choi, Sungwook,Wilson, Ian A.,Powers, Evan T.,Kelly, Jeffery W.,Johnson, Steven M. Pergamon Press 2017 Bioorganic & medicinal chemistry letters Vol.27 No.15

<P><B>Abstract</B></P> <P>Rate-limiting dissociation of the tetrameric protein transthyretin (TTR), followed by monomer misfolding and misassembly, appears to cause degenerative diseases in humans known as the transthyretin amyloidoses, based on human genetic, biochemical and pharmacologic evidence. Small molecules that bind to the generally unoccupied thyroxine binding pockets in the native TTR tetramer kinetically stabilize the tetramer, slowing subunit dissociation proportional to the extent that the molecules stabilize the native state over the dissociative transition state—thereby inhibiting amyloidogenesis. Herein, we use previously reported structure-activity relationship data to develop two semi-quantitative algorithms for identifying the structures of potent and selective transthyretin kinetic stabilizers/amyloidogenesis inhibitors. The viability of these prediction algorithms, in particular the more robust <I>in silico</I> docking model, is perhaps best validated by the clinical success of tafamidis, the first-in-class drug approved in Europe, Japan, South America, and elsewhere for treating transthyretin aggregation-associated familial amyloid polyneuropathy. Tafamidis is also being evaluated in a fully-enrolled placebo-controlled clinical trial for its efficacy against TTR cardiomyopathy. These prediction algorithms will be useful for identifying second generation TTR kinetic stabilizers, should these be needed to ameliorate the central nervous system or ophthalmologic pathology caused by TTR aggregation in organs not accessed by oral tafamidis administration.</P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

The Predictive QSAR Model for hERG Inhibitors Using Bayesian and Random Forest Classification Method

Jun Hyoung Kim,Chong Hak Chae,Shin Myung Kang,Joo Yon Lee,Gil Nam Lee,Soon Hee Hwang,강남숙 대한화학회 2011 Bulletin of the Korean Chemical Society Vol.32 No.4

In this study, we have developed a ligand-based in-silico prediction model to classify chemical structures into hERG blockers using Bayesian and random forest modeling methods. These models were built based on patch clamp experimental results. The findings presented in this work indicate that Laplacian-modified naïve Bayesian classification with diverse selection is useful for predicting hERG inhibitors when a large data set is not obtained.

The Predictive QSAR Model for hERG Inhibitors Using Bayesian and Random Forest Classification Method

Kim, Jun-Hyoung,Chae, Chong-Hak,Kang, Shin-Myung,Lee, Joo-Yon,Lee, Gil-Nam,Hwang, Soon-Hee,Kang, Nam-Sook Korean Chemical Society 2011 Bulletin of the Korean Chemical Society Vol.32 No.4

In this study, we have developed a ligand-based in-silico prediction model to classify chemical structures into hERG blockers using Bayesian and random forest modeling methods. These models were built based on patch clamp experimental results. The findings presented in this work indicate that Laplacian-modified naive Bayesian classification with diverse selection is useful for predicting hERG inhibitors when a large data set is not obtained.

Review of toxicity prediction studies using toxicity database

Jaeseong Jeong(정재성),Jinhee Choi(최진희) 환경독성보건학회 2021 한국독성학회 심포지움 및 학술발표회 Vol.2021 No.5

Recently, computational toxicology has emerged that predicts toxicity without conducting toxicity tests at all. This has become possible due to the rapid development of computer technology, and various computational toxicology techniques such as quantitative structure-activity relationship (QSAR) that predict toxicity based on the structure of chemical substances are attracting attention. Currently, research are underway to apply artificial intelligence techniques used to process big data in various fields to toxicology, mainly in scientifically advanced countries. The competition for the development of toxicity prediction models using artificial intelligence is accelerating, and techniques are becoming increasingly complex. To develop a toxicity prediction model using artificial intelligence and use it for regulation, it is necessary to understand the recent development. In this study, we analyze toxicity prediction studies using artificial intelligence techniques, and summarize artificial intelligence algorithms and prediction performance used in recent papers. We have analyzed over 70 papers published since 2014. Models have been developed to predict about 30 different toxicity endpoints using more than 20 toxicity databases. In the development of the model, MACCS fingerprint and random forest algorithms were used the most. The use of artificial intelligence techniques in the development of toxicity prediction models is a fairly new challenge, requiring active and diverse efforts toward a scientific accord and regulatory application. The comprehensive overview provided in this study could be used as a useful guide to further development and application of toxicity prediction models.

In silico prediction of potential chemical reactions mediated by human enzymes

Yu, Myeong-Sang,Lee, Hyang-Mi,Park, Aaron,Park, Chungoo,Ceong, Hyithaek,Rhee, Ki-Hyeong,Na, Dokyun BioMed Central 2018 BMC bioinformatics Vol.19 No.-

<P><B>Background</B></P><P>Administered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms.</P><P><B>Result</B></P><P>We developed an in silico model to predict which of human enzymes including metabolic enzymes as well as CYP450 family can catalyze a given chemical compound. The prediction is based on the chemical and physical similarity between known enzyme substrates and a query chemical compound. Our in silico model was developed using multiple linear regression and the model showed high performance (AUC = 0.896) despite of the large number of enzymes. When evaluated on a test dataset, it also showed significantly high performance (AUC = 0.746). Interestingly, evaluation with literature data showed that our model can be used to predict not only enzymatic reactions but also drug conversion and enzyme inhibition.</P><P><B>Conclusion</B></P><P>Our model was able to predict enzymatic reactions of a query molecule with a high accuracy. This may foster to discover new metabolic routes and to accelerate the computational development of drug candidates by enabling the prediction of the potential conversion of administered drugs into active or inactive forms.</P>

Construction of a predictive model for evaluating multiple organ toxicity

안유리,김재영,김양석,김양석 대한독성 유전단백체 학회 2016 Molecular & cellular toxicology Vol.12 No.1

The liver and kidneys are major target organs that suffer in adverse drug reactions, and liver and kidney toxicity are often present together. A multiple organ toxicological study is more helpful in understanding the effects of drugs in living systems than targeting a specific organ for a toxicity study. There are many prediction models for evaluating toxicity, but they are limited by single organ predictions and insufficient to understand the toxic mechanisms of drugs in the human body. Thus, we developed multiple organ toxicity prediction models and sought to lay a foundation for understanding the toxic effect of drugs on other organs, apart from the target organ. Here, we developed and evaluated the four computational prediction models (ANN, kNN, LDA, and SVM) that can predict whether a drug is liver toxic or liver-kidney toxic. To construct the predictive model, we extracted 210 molecular signatures of two classes of 108 drugs from TG-gate transcriptome data. Among the four algorithms, SVM was the ‘best’ method for multi-organ toxicity classification, with over 90% accuracy and the maximum power of classification with a small number of features. These bioinformatics tools will help researchers to recognize the side toxicity of drugs, not just in the target organ, before advancing them to clinical trials and exposing humans.

In silico analysis of Brucella abortus Omp2b and in vitro expression of SOmp2b

Maryam Golshani,Nafise Vaeznia,Mehdi Sahmani,Saeid Bouzari 대한백신학회 2016 Clinical and Experimental Vaccine Research Vol.5 No.1

Purpose: At present, there is no vaccine available for the prevention of human brucellosis. Brucella outer membrane protein 2b (Omp2b) is a 36 kD porin existed in common Brucella pathogens and it is considered as priority antigen for designing a new subunit vaccine. Materials and Methods: In the current study, we aimed to predict and analyze the secondary and tertiary structures of the Brucella abortus Omp2b protein, and to predict T-cell and B-cell epitopes with the help of bioinformatics tools. Subsequently, cloning and expression of the short form of Omp2b (SOmp2b) was performed using pET28a expression vector and Escherichia coli BL21 host, respectively. The recombinant SOmp2b (rSOmp2b) was purified with Ni-NTA column. Results: The recombinant protein was successfully expressed in E. coli host and purified under denaturation conditions. The yield of the purified rSOmp2b was estimated by Bradford method and found to be 220 μg/mL of the culture. Conclusion: Our results indicate that Omp2b protein has a potential to induce both B-cell. and T-cell.mediated immune responses and it can be evaluated as a new subunit vaccine candidate against brucellosis.

In silico Protein-ligand Interaction Prediction and its Applications

Jinsol YANG 한국생물공학회 2022 한국생물공학회 학술대회 Vol.2022 No.9